CSE 7/5337: Information Retrieval and Web Search Web Search (IIR 19) Michael Hahsler Southern Methodist University These slides are largely based on the slides by Hinrich Sch¨ utze Institute for Natural Language Processing, University of Stuttgart http://informationretrieval.org Spring 2012 Hahsler (SMU) CSE 7/5337 Spring 2012 1 / 78
Overview Big picture 1 Ads 2 Duplicate detection 3 Spam 4 Web IR 5 Queries Links Context Users Documents Size Hahsler (SMU) CSE 7/5337 Spring 2012 2 / 78
Outline Big picture 1 Ads 2 Duplicate detection 3 Spam 4 Web IR 5 Queries Links Context Users Documents Size Hahsler (SMU) CSE 7/5337 Spring 2012 3 / 78
Web search overview Hahsler (SMU) CSE 7/5337 Spring 2012 4 / 78
Search is a top activity on the web Hahsler (SMU) CSE 7/5337 Spring 2012 5 / 78
Without search engines, the web wouldn’t work Without search, content is hard to find. → Without search, there is no incentive to create content. ◮ Why publish something if nobody will read it? ◮ Why publish something if I don’t get ad revenue from it? Somebody needs to pay for the web. ◮ Servers, web infrastructure, content creation ◮ A large part today is paid by search ads. ◮ Search pays for the web. Hahsler (SMU) CSE 7/5337 Spring 2012 6 / 78
Interest aggregation Unique feature of the web: A small number of geographically dispersed people with similar interests can find each other. ◮ Elementary school kids with hemophilia ◮ People interested in translating R5R5 Scheme into relatively portable C (open source project) ◮ Search engines are a key enabler for interest aggregation. Hahsler (SMU) CSE 7/5337 Spring 2012 7 / 78
IR on the web vs. IR in general On the web, search is not just a nice feature. ◮ Search is a key enabler of the web: . . . ◮ . . . financing, content creation, interest aggregation etc. → look at search ads The web is a chaotic und uncoordinated collection. → lots of duplicates – need to detect duplicates No control / restrictions on who can author content → lots of spam – need to detect spam The web is very large. → need to know how big it is Hahsler (SMU) CSE 7/5337 Spring 2012 8 / 78
Take-away today Ads – they pay for the web Duplicate detection – addresses one aspect of chaotic content creation Spam detection – addresses one aspect of lack of central access control Probably won’t get to today ◮ Web information retrieval ◮ Size of the web Hahsler (SMU) CSE 7/5337 Spring 2012 9 / 78
Outline Big picture 1 Ads 2 Duplicate detection 3 Spam 4 Web IR 5 Queries Links Context Users Documents Size Hahsler (SMU) CSE 7/5337 Spring 2012 10 / 78
First generation of search ads: Goto (1996) Hahsler (SMU) CSE 7/5337 Spring 2012 11 / 78
First generation of search ads: Goto (1996) Buddy Blake bid the maximum ($0.38) for this search. He paid $0.38 to Goto every time somebody clicked on the link. Pages were simply ranked according to bid – revenue maximization for Goto. No separation of ads/docs. Only one result list! Upfront and honest. No relevance ranking, . . . . . . but Goto did not pretend there was any. Hahsler (SMU) CSE 7/5337 Spring 2012 12 / 78
Second generation of search ads: Google (2000/2001) Strict separation of search results and search ads Hahsler (SMU) CSE 7/5337 Spring 2012 13 / 78
Two ranked lists: web pages (left) and ads (right) Hahsler (SMU) CSE 7/5337 Spring 2012 14 / 78
Do ads influence editorial content? Similar problem at newspapers / TV channels A newspaper is reluctant to publish harsh criticism of its major advertisers. The line often gets blurred at newspapers / on TV. No known case of this happening with search engines yet? Hahsler (SMU) CSE 7/5337 Spring 2012 15 / 78
How are the ads on the right ranked? Hahsler (SMU) CSE 7/5337 Spring 2012 16 / 78
How are ads ranked? Advertisers bid for keywords – sale by auction. Open system: Anybody can participate and bid on keywords. Advertisers are only charged when somebody clicks on your ad. How does the auction determine an ad’s rank and the price paid for the ad? Basis is a second price auction, but with twists For the bottom line, this is perhaps the most important research area for search engines – computational advertising. ◮ Squeezing an additional fraction of a cent from each ad means billions of additional revenue for the search engine. Hahsler (SMU) CSE 7/5337 Spring 2012 17 / 78
How are ads ranked? First cut: according to bid price ` a la Goto ◮ Bad idea: open to abuse ◮ Example: query [does my husband cheat?] → ad for divorce lawyer ◮ We don’t want to show nonrelevant or offensive ads. Instead: rank based on bid price and relevance Key measure of ad relevance: clickthrough rate ◮ clickthrough rate = CTR = clicks per impressions Result: A nonrelevant ad will be ranked low. ◮ Even if this decreases search engine revenue short-term ◮ Hope: Overall acceptance of the system and overall revenue is maximized if users get useful information. Other ranking factors: location, time of day, quality and loading speed of landing page The main ranking factor: the query Hahsler (SMU) CSE 7/5337 Spring 2012 18 / 78
Google AdWords demo Hahsler (SMU) CSE 7/5337 Spring 2012 19 / 78
Google’s second price auction advertiser bid CTR ad rank rank paid A $4.00 0.01 0.04 4 (minimum) B $3.00 0.03 0.09 2 $2.68 C $2.00 0.06 0.12 1 $1.51 D $1.00 0.08 0.08 3 $0.51 bid: maximum bid for a click by advertiser CTR: click-through rate: when an ad is displayed, what percentage of time do users click on it? CTR is a measure of relevance. ad rank: bid × CTR: this trades off (i) how much money the advertiser is willing to pay against (ii) how relevant the ad is rank: rank in auction paid: second price auction price paid by advertiser Second price auction: The advertiser pays the minimum amount necessary to maintain their position in the auction (plus 1 cent). Hahsler (SMU) CSE 7/5337 Spring 2012 20 / 78 price 1 × CTR 1 = bid 2 × CTR 2 (this will result in rank 1 =rank 2 )
Keywords with high bids According to http://www.cwire.org/highest-paying-search-terms/ $69.1 mesothelioma treatment options $65.9 personal injury lawyer michigan $62.6 student loans consolidation $61.4 car accident attorney los angeles $59.4 online car insurance quotes $59.4 arizona dui lawyer $46.4 asbestos cancer $40.1 home equity line of credit $39.8 life insurance quotes $39.2 refinancing $38.7 equity line of credit $38.0 lasik eye surgery new york city $37.0 2nd mortgage $35.9 free car insurance quote Hahsler (SMU) CSE 7/5337 Spring 2012 21 / 78
Search ads: A win-win-win? The search engine company gets revenue every time somebody clicks on an ad. The user only clicks on an ad if they are interested in the ad. ◮ Search engines punish misleading and nonrelevant ads. ◮ As a result, users are often satisfied with what they find after clicking on an ad. The advertiser finds new customers in a cost-effective way. Hahsler (SMU) CSE 7/5337 Spring 2012 22 / 78
Exercise Why is web search potentially more attractive for advertisers than TV spots, newspaper ads or radio spots? The advertiser pays for all this. How can the advertiser be cheated? Any way this could be bad for the user? Any way this could be bad for the search engine? Hahsler (SMU) CSE 7/5337 Spring 2012 23 / 78
Not a win-win-win: Keyword arbitrage Buy a keyword on Google Then redirect traffic to a third party that is paying much more than you are paying Google. ◮ E.g., redirect to a page full of ads This rarely makes sense for the user. Ad spammers keep inventing new tricks. The search engines need time to catch up with them. Hahsler (SMU) CSE 7/5337 Spring 2012 24 / 78
Not a win-win-win: Violation of trademarks Example: geico During part of 2005: The search term “geico” on Google was bought by competitors. Geico lost this case in the United States. Louis Vuitton lost similar case in Europe. It’s potentially misleading to users to trigger an ad off of a trademark if the user can’t buy the product on the site. Hahsler (SMU) CSE 7/5337 Spring 2012 25 / 78
Outline Big picture 1 Ads 2 Duplicate detection 3 Spam 4 Web IR 5 Queries Links Context Users Documents Size Hahsler (SMU) CSE 7/5337 Spring 2012 26 / 78
Duplicate detection The web is full of duplicated content. More so than many other collections Exact duplicates ◮ Easy to eliminate ◮ E.g., use hash/fingerprint Near-duplicates ◮ Abundant on the web ◮ Difficult to eliminate For the user, it’s annoying to get a search result with near-identical documents. Marginal relevance is zero: even a highly relevant document becomes nonrelevant if it appears below a (near-)duplicate. We need to eliminate near-duplicates. Hahsler (SMU) CSE 7/5337 Spring 2012 27 / 78
Near-duplicates: Example Hahsler (SMU) CSE 7/5337 Spring 2012 28 / 78
Exercise How would you eliminate near-duplicates on the web? Hahsler (SMU) CSE 7/5337 Spring 2012 29 / 78
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